Cost‐effective long‐term groundwater monitoring design using a genetic algorithm and global mass interpolation

Abstract

A new methodology for sampling plan design has been developed to reduce the costs associated with long‐term monitoring of sites with groundwater contamination. The method combines a fate‐and‐transport model, plume interpolation, and a genetic algorithm to identify cost‐effective sampling plans that accurately quantify the total mass of dissolved contaminant. The plume interpolation methods considered were inverse‐distance weighting, ordinary kriging, and a hybrid method that combines the two approaches. Application of the methodology to Hill Air Force Base indicated that sampling costs could be reduced by as much as 60% without significant loss in accuracy of the global mass estimates. Inverse‐distance weighting was shown to be most effective as a screening tool for evaluating whether more comprehensive geostatistical modeling is warranted. The hybrid method was effective for implementing such a tiered approach, reducing computational time by more than 60% relative to kriging alone.